Overview

Dataset statistics

Number of variables27
Number of observations355
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory75.0 KiB
Average record size in memory216.4 B

Variable types

Numeric21
Categorical6

Alerts

season has constant value "Pre-monsoon 2020"Constant
mandal has a high cardinality: 299 distinct valuesHigh cardinality
village has a high cardinality: 350 distinct valuesHigh cardinality
sno is highly overall correlated with temp_id and 1 other fieldsHigh correlation
temp_id is highly overall correlated with sno and 1 other fieldsHigh correlation
long_gis is highly overall correlated with districtHigh correlation
lat_gis is highly overall correlated with districtHigh correlation
pH is highly overall correlated with CO3 and 1 other fieldsHigh correlation
E.C is highly overall correlated with TDS and 8 other fieldsHigh correlation
TDS is highly overall correlated with E.C and 8 other fieldsHigh correlation
CO3 is highly overall correlated with pHHigh correlation
HCO3 is highly overall correlated with E.C and 2 other fieldsHigh correlation
Cl is highly overall correlated with E.C and 7 other fieldsHigh correlation
NO3 is highly overall correlated with T.H and 1 other fieldsHigh correlation
SO4 is highly overall correlated with E.C and 5 other fieldsHigh correlation
Na is highly overall correlated with E.C and 6 other fieldsHigh correlation
Ca is highly overall correlated with pH and 5 other fieldsHigh correlation
Mg is highly overall correlated with E.C and 5 other fieldsHigh correlation
T.H is highly overall correlated with E.C and 6 other fieldsHigh correlation
SAR is highly overall correlated with E.C and 3 other fieldsHigh correlation
RSC meq / L is highly overall correlated with Cl and 5 other fieldsHigh correlation
district is highly overall correlated with sno and 3 other fieldsHigh correlation
Classification is highly overall correlated with SO4 and 2 other fieldsHigh correlation
Classification.1 is highly overall correlated with RSC meq / LHigh correlation
Classification is highly imbalanced (50.9%)Imbalance
Classification.1 is highly imbalanced (66.2%)Imbalance
mandal is uniformly distributedUniform
village is uniformly distributedUniform
sno has unique valuesUnique
temp_id has unique valuesUnique
CO3 has 292 (82.3%) zerosZeros

Reproduction

Analysis started2023-08-16 12:35:01.444158
Analysis finished2023-08-16 12:37:31.582262
Duration2 minutes and 30.14 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

sno
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct355
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.32113
Minimum1
Maximum379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-16T18:07:31.863517image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.7
Q191.5
median193
Q3289.5
95-th percentile361.3
Maximum379
Range378
Interquartile range (IQR)198

Descriptive statistics

Standard deviation111.15685
Coefficient of variation (CV)0.57797525
Kurtosis-1.231229
Mean192.32113
Median Absolute Deviation (MAD)99
Skewness-0.036231533
Sum68274
Variance12355.846
MonotonicityStrictly increasing
2023-08-16T18:07:32.285380image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
290 1
 
0.3%
264 1
 
0.3%
263 1
 
0.3%
262 1
 
0.3%
261 1
 
0.3%
260 1
 
0.3%
259 1
 
0.3%
258 1
 
0.3%
257 1
 
0.3%
Other values (345) 345
97.2%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
379 1
0.3%
378 1
0.3%
377 1
0.3%
376 1
0.3%
375 1
0.3%
374 1
0.3%
373 1
0.3%
372 1
0.3%
371 1
0.3%
370 1
0.3%

district
Categorical

Distinct33
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
NALGONDA
33 
NIZAMABAD
 
23
KAMAREDDY
 
21
RANGAREDDY
 
17
MEDAK
 
17
Other values (28)
244 

Length

Max length17
Median length12
Mean length9.2056338
Min length5

Characters and Unicode

Total characters3268
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowADILABAD
2nd rowADILABAD
3rd rowADILABAD
4th rowADILABAD
5th rowADILABAD

Common Values

ValueCountFrequency (%)
NALGONDA 33
 
9.3%
NIZAMABAD 23
 
6.5%
KAMAREDDY 21
 
5.9%
RANGAREDDY 17
 
4.8%
MEDAK 17
 
4.8%
VIKARABAD 16
 
4.5%
YADADRI 15
 
4.2%
BHADRADRI 14
 
3.9%
JAGITYAL 14
 
3.9%
MAHABUBNAGAR 12
 
3.4%
Other values (23) 173
48.7%

Length

2023-08-16T18:07:32.676011image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nalgonda 33
 
8.7%
nizamabad 23
 
6.1%
kamareddy 21
 
5.5%
warangal 18
 
4.7%
rangareddy 17
 
4.5%
medak 17
 
4.5%
vikarabad 16
 
4.2%
yadadri 15
 
4.0%
bhadradri 14
 
3.7%
jagityal 14
 
3.7%
Other values (25) 191
50.4%

Most occurring characters

ValueCountFrequency (%)
A 782
23.9%
D 328
10.0%
R 258
 
7.9%
N 231
 
7.1%
L 170
 
5.2%
M 161
 
4.9%
G 153
 
4.7%
I 141
 
4.3%
B 138
 
4.2%
E 127
 
3.9%
Other values (16) 779
23.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3175
97.2%
Space Separator 35
 
1.1%
Open Punctuation 29
 
0.9%
Close Punctuation 29
 
0.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 782
24.6%
D 328
10.3%
R 258
 
8.1%
N 231
 
7.3%
L 170
 
5.4%
M 161
 
5.1%
G 153
 
4.8%
I 141
 
4.4%
B 138
 
4.3%
E 127
 
4.0%
Other values (13) 686
21.6%
Space Separator
ValueCountFrequency (%)
35
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3175
97.2%
Common 93
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 782
24.6%
D 328
10.3%
R 258
 
8.1%
N 231
 
7.3%
L 170
 
5.4%
M 161
 
5.1%
G 153
 
4.8%
I 141
 
4.4%
B 138
 
4.3%
E 127
 
4.0%
Other values (13) 686
21.6%
Common
ValueCountFrequency (%)
35
37.6%
( 29
31.2%
) 29
31.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3268
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 782
23.9%
D 328
10.0%
R 258
 
7.9%
N 231
 
7.1%
L 170
 
5.2%
M 161
 
4.9%
G 153
 
4.7%
I 141
 
4.3%
B 138
 
4.2%
E 127
 
3.9%
Other values (16) 779
23.8%

mandal
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct299
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
Anumula
 
4
Chandur
 
4
Rajapet
 
4
Nandipet
 
3
Dharoor
 
3
Other values (294)
337 

Length

Max length18
Median length16
Mean length8.8619718
Min length4

Characters and Unicode

Total characters3146
Distinct characters54
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique257 ?
Unique (%)72.4%

Sample

1st rowAdilabad
2nd rowBazarhatnur
3rd rowGudihatnoor
4th rowJainath
5th rowNarnoor

Common Values

ValueCountFrequency (%)
Anumula 4
 
1.1%
Chandur 4
 
1.1%
Rajapet 4
 
1.1%
Nandipet 3
 
0.8%
Dharoor 3
 
0.8%
CC Kunta 3
 
0.8%
Shivampet 3
 
0.8%
Narayanpet 3
 
0.8%
Nalgonda 3
 
0.8%
Nakrekal 3
 
0.8%
Other values (289) 322
90.7%

Length

2023-08-16T18:07:33.035385image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
anumula 4
 
1.1%
rajapet 4
 
1.1%
chandur 4
 
1.1%
narayanpet 3
 
0.8%
qutubullapur 3
 
0.8%
nakrekal 3
 
0.8%
nalgonda 3
 
0.8%
ghanpur 3
 
0.8%
shivampet 3
 
0.8%
cc 3
 
0.8%
Other values (294) 334
91.0%

Most occurring characters

ValueCountFrequency (%)
a 626
19.9%
l 225
 
7.2%
r 223
 
7.1%
u 168
 
5.3%
n 165
 
5.2%
d 162
 
5.1%
e 142
 
4.5%
i 135
 
4.3%
p 135
 
4.3%
h 113
 
3.6%
Other values (44) 1052
33.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2720
86.5%
Uppercase Letter 394
 
12.5%
Space Separator 17
 
0.5%
Other Punctuation 7
 
0.2%
Open Punctuation 3
 
0.1%
Close Punctuation 3
 
0.1%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 626
23.0%
l 225
 
8.3%
r 223
 
8.2%
u 168
 
6.2%
n 165
 
6.1%
d 162
 
6.0%
e 142
 
5.2%
i 135
 
5.0%
p 135
 
5.0%
h 113
 
4.2%
Other values (15) 626
23.0%
Uppercase Letter
ValueCountFrequency (%)
K 49
12.4%
M 41
 
10.4%
N 38
 
9.6%
A 26
 
6.6%
B 24
 
6.1%
T 23
 
5.8%
C 23
 
5.8%
D 22
 
5.6%
P 20
 
5.1%
G 20
 
5.1%
Other values (14) 108
27.4%
Space Separator
ValueCountFrequency (%)
17
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3114
99.0%
Common 32
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 626
20.1%
l 225
 
7.2%
r 223
 
7.2%
u 168
 
5.4%
n 165
 
5.3%
d 162
 
5.2%
e 142
 
4.6%
i 135
 
4.3%
p 135
 
4.3%
h 113
 
3.6%
Other values (39) 1020
32.8%
Common
ValueCountFrequency (%)
17
53.1%
. 7
21.9%
( 3
 
9.4%
) 3
 
9.4%
- 2
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 626
19.9%
l 225
 
7.2%
r 223
 
7.1%
u 168
 
5.3%
n 165
 
5.2%
d 162
 
5.1%
e 142
 
4.5%
i 135
 
4.3%
p 135
 
4.3%
h 113
 
3.6%
Other values (44) 1052
33.4%

village
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct350
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
Angadipet
 
2
Khanapur
 
2
Kodur
 
2
Kanchanapalli
 
2
Dharoor
 
2
Other values (345)
345 

Length

Max length21
Median length16
Mean length9.3802817
Min length4

Characters and Unicode

Total characters3330
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique345 ?
Unique (%)97.2%

Sample

1st rowAdilabad
2nd rowBazarhatnur
3rd rowGudihatnoor
4th rowJainath
5th rowNarnoor

Common Values

ValueCountFrequency (%)
Angadipet 2
 
0.6%
Khanapur 2
 
0.6%
Kodur 2
 
0.6%
Kanchanapalli 2
 
0.6%
Dharoor 2
 
0.6%
Yanampally 1
 
0.3%
Gannaram 1
 
0.3%
Dupally 1
 
0.3%
Arsapally 1
 
0.3%
Velmal 1
 
0.3%
Other values (340) 340
95.8%

Length

2023-08-16T18:07:33.457268image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i 3
 
0.8%
angadipet 2
 
0.5%
nagaram 2
 
0.5%
2 2
 
0.5%
1 2
 
0.5%
tandur 2
 
0.5%
nagar 2
 
0.5%
somaram 2
 
0.5%
b 2
 
0.5%
s 2
 
0.5%
Other values (359) 363
94.5%

Most occurring characters

ValueCountFrequency (%)
a 683
20.5%
l 272
 
8.2%
r 230
 
6.9%
u 175
 
5.3%
n 162
 
4.9%
p 159
 
4.8%
d 142
 
4.3%
e 140
 
4.2%
i 137
 
4.1%
m 128
 
3.8%
Other values (46) 1102
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2870
86.2%
Uppercase Letter 392
 
11.8%
Space Separator 31
 
0.9%
Other Punctuation 12
 
0.4%
Open Punctuation 9
 
0.3%
Close Punctuation 9
 
0.3%
Decimal Number 7
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 683
23.8%
l 272
 
9.5%
r 230
 
8.0%
u 175
 
6.1%
n 162
 
5.6%
p 159
 
5.5%
d 142
 
4.9%
e 140
 
4.9%
i 137
 
4.8%
m 128
 
4.5%
Other values (15) 642
22.4%
Uppercase Letter
ValueCountFrequency (%)
M 45
11.5%
K 45
11.5%
B 32
 
8.2%
N 30
 
7.7%
A 29
 
7.4%
S 26
 
6.6%
R 24
 
6.1%
P 23
 
5.9%
D 21
 
5.4%
G 19
 
4.8%
Other values (14) 98
25.0%
Decimal Number
ValueCountFrequency (%)
1 3
42.9%
2 3
42.9%
5 1
 
14.3%
Space Separator
ValueCountFrequency (%)
31
100.0%
Other Punctuation
ValueCountFrequency (%)
. 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3262
98.0%
Common 68
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 683
20.9%
l 272
 
8.3%
r 230
 
7.1%
u 175
 
5.4%
n 162
 
5.0%
p 159
 
4.9%
d 142
 
4.4%
e 140
 
4.3%
i 137
 
4.2%
m 128
 
3.9%
Other values (39) 1034
31.7%
Common
ValueCountFrequency (%)
31
45.6%
. 12
 
17.6%
( 9
 
13.2%
) 9
 
13.2%
1 3
 
4.4%
2 3
 
4.4%
5 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 683
20.5%
l 272
 
8.2%
r 230
 
6.9%
u 175
 
5.3%
n 162
 
4.9%
p 159
 
4.8%
d 142
 
4.3%
e 140
 
4.2%
i 137
 
4.1%
m 128
 
3.8%
Other values (46) 1102
33.1%

temp_id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct355
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1506.8676
Minimum1001
Maximum4012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-16T18:07:33.863507image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1055.5
Q11232.5
median1452
Q31670.5
95-th percentile1877.9
Maximum4012
Range3011
Interquartile range (IQR)438

Descriptive statistics

Standard deviation404.87009
Coefficient of variation (CV)0.26868326
Kurtosis8.8343225
Mean1506.8676
Median Absolute Deviation (MAD)220
Skewness2.415835
Sum534938
Variance163919.79
MonotonicityNot monotonic
2023-08-16T18:07:34.254142image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001 1
 
0.3%
1625 1
 
0.3%
1542 1
 
0.3%
1556 1
 
0.3%
1559 1
 
0.3%
1547 1
 
0.3%
1543 1
 
0.3%
1562 1
 
0.3%
1555 1
 
0.3%
1541 1
 
0.3%
Other values (345) 345
97.2%
ValueCountFrequency (%)
1001 1
0.3%
1002 1
0.3%
1007 1
0.3%
1009 1
0.3%
1010 1
0.3%
1011 1
0.3%
1013 1
0.3%
1014 1
0.3%
1015 1
0.3%
1021 1
0.3%
ValueCountFrequency (%)
4012 1
0.3%
3137 1
0.3%
3117 1
0.3%
3088 1
0.3%
3041 1
0.3%
3040 1
0.3%
3036 1
0.3%
3035 1
0.3%
3034 1
0.3%
3029 1
0.3%

long_gis
Real number (ℝ)

Distinct353
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.749243
Minimum77.444
Maximum80.900922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-16T18:07:34.707263image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum77.444
5-th percentile77.65529
Q178.1485
median78.552568
Q379.290817
95-th percentile80.203022
Maximum80.900922
Range3.456922
Interquartile range (IQR)1.142317

Descriptive statistics

Standard deviation0.77995618
Coefficient of variation (CV)0.0099043006
Kurtosis-0.29372528
Mean78.749243
Median Absolute Deviation (MAD)0.528968
Skewness0.58855508
Sum27955.981
Variance0.60833164
MonotonicityNot monotonic
2023-08-16T18:07:35.129137image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.114 2
 
0.6%
78.508 2
 
0.6%
77.903 1
 
0.3%
78.396 1
 
0.3%
78.426 1
 
0.3%
77.8575 1
 
0.3%
77.991 1
 
0.3%
78.06863 1
 
0.3%
78.143 1
 
0.3%
78.2182 1
 
0.3%
Other values (343) 343
96.6%
ValueCountFrequency (%)
77.444 1
0.3%
77.4812 1
0.3%
77.500096 1
0.3%
77.5003 1
0.3%
77.511 1
0.3%
77.516956 1
0.3%
77.526 1
0.3%
77.5415 1
0.3%
77.5757 1
0.3%
77.591 1
0.3%
ValueCountFrequency (%)
80.900922 1
0.3%
80.826104 1
0.3%
80.809241 1
0.3%
80.7911 1
0.3%
80.73 1
0.3%
80.619 1
0.3%
80.6106 1
0.3%
80.58 1
0.3%
80.568147 1
0.3%
80.520318 1
0.3%

lat_gis
Real number (ℝ)

Distinct351
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.720789
Minimum15.896441
Maximum19.730555
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-16T18:07:35.597903image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum15.896441
5-th percentile16.335238
Q117.121656
median17.6713
Q318.372871
95-th percentile19.145108
Maximum19.730555
Range3.834114
Interquartile range (IQR)1.251215

Descriptive statistics

Standard deviation0.86635719
Coefficient of variation (CV)0.048889312
Kurtosis-0.70953124
Mean17.720789
Median Absolute Deviation (MAD)0.630491
Skewness0.1361187
Sum6290.8802
Variance0.75057477
MonotonicityNot monotonic
2023-08-16T18:07:36.035386image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.54 2
 
0.6%
17.33 2
 
0.6%
17.47 2
 
0.6%
17.445 2
 
0.6%
19.6683 1
 
0.3%
18.912998 1
 
0.3%
18.6102 1
 
0.3%
18.733 1
 
0.3%
18.685342 1
 
0.3%
18.831 1
 
0.3%
Other values (341) 341
96.1%
ValueCountFrequency (%)
15.896441 1
0.3%
15.94 1
0.3%
15.9656 1
0.3%
16.01518 1
0.3%
16.0701 1
0.3%
16.107 1
0.3%
16.1156 1
0.3%
16.1287 1
0.3%
16.129667 1
0.3%
16.179 1
0.3%
ValueCountFrequency (%)
19.730555 1
0.3%
19.6811 1
0.3%
19.6683 1
0.3%
19.6334 1
0.3%
19.525555 1
0.3%
19.5219 1
0.3%
19.495665 1
0.3%
19.458888 1
0.3%
19.444 1
0.3%
19.3789 1
0.3%

gwl
Real number (ℝ)

Distinct317
Distinct (%)89.8%
Missing2
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean11.989292
Minimum1.4
Maximum49.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-16T18:07:36.504167image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile3.412
Q16.2
median9.88
Q315.99
95-th percentile26.584
Maximum49.11
Range47.71
Interquartile range (IQR)9.79

Descriptive statistics

Standard deviation7.6447749
Coefficient of variation (CV)0.63763357
Kurtosis1.7925611
Mean11.989292
Median Absolute Deviation (MAD)4.58
Skewness1.2236689
Sum4232.22
Variance58.442583
MonotonicityNot monotonic
2023-08-16T18:07:36.957264image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.9 4
 
1.1%
7.38 3
 
0.8%
4.5 3
 
0.8%
9.9 3
 
0.8%
18.1 3
 
0.8%
8 3
 
0.8%
16.5 3
 
0.8%
14.15 2
 
0.6%
13.84 2
 
0.6%
8.15 2
 
0.6%
Other values (307) 325
91.5%
ValueCountFrequency (%)
1.4 1
0.3%
1.55 1
0.3%
1.7 1
0.3%
1.76 1
0.3%
2.17 1
0.3%
2.22 1
0.3%
2.27 1
0.3%
2.43 1
0.3%
2.47 1
0.3%
2.57 1
0.3%
ValueCountFrequency (%)
49.11 1
0.3%
39.1 1
0.3%
37.8 1
0.3%
33.82 1
0.3%
33 2
0.6%
32 1
0.3%
31.43 1
0.3%
31.3 1
0.3%
31.2 1
0.3%
29.37 1
0.3%

season
Categorical

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
Pre-monsoon 2020
355 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters5680
Distinct characters11
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPre-monsoon 2020
2nd rowPre-monsoon 2020
3rd rowPre-monsoon 2020
4th rowPre-monsoon 2020
5th rowPre-monsoon 2020

Common Values

ValueCountFrequency (%)
Pre-monsoon 2020 355
100.0%

Length

2023-08-16T18:07:37.332258image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-16T18:07:37.707293image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
pre-monsoon 355
50.0%
2020 355
50.0%

Most occurring characters

ValueCountFrequency (%)
o 1065
18.8%
n 710
12.5%
2 710
12.5%
0 710
12.5%
P 355
 
6.2%
r 355
 
6.2%
e 355
 
6.2%
- 355
 
6.2%
m 355
 
6.2%
s 355
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3195
56.2%
Decimal Number 1420
25.0%
Uppercase Letter 355
 
6.2%
Dash Punctuation 355
 
6.2%
Space Separator 355
 
6.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1065
33.3%
n 710
22.2%
r 355
 
11.1%
e 355
 
11.1%
m 355
 
11.1%
s 355
 
11.1%
Decimal Number
ValueCountFrequency (%)
2 710
50.0%
0 710
50.0%
Uppercase Letter
ValueCountFrequency (%)
P 355
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 355
100.0%
Space Separator
ValueCountFrequency (%)
355
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3550
62.5%
Common 2130
37.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1065
30.0%
n 710
20.0%
P 355
 
10.0%
r 355
 
10.0%
e 355
 
10.0%
m 355
 
10.0%
s 355
 
10.0%
Common
ValueCountFrequency (%)
2 710
33.3%
0 710
33.3%
- 355
16.7%
355
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1065
18.8%
n 710
12.5%
2 710
12.5%
0 710
12.5%
P 355
 
6.2%
r 355
 
6.2%
e 355
 
6.2%
- 355
 
6.2%
m 355
 
6.2%
s 355
 
6.2%

pH
Real number (ℝ)

Distinct137
Distinct (%)38.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0085352
Minimum7.02
Maximum9.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-16T18:07:38.019776image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum7.02
5-th percentile7.4
Q17.76
median7.99
Q38.24
95-th percentile8.703
Maximum9.37
Range2.35
Interquartile range (IQR)0.48

Descriptive statistics

Standard deviation0.38149216
Coefficient of variation (CV)0.047635697
Kurtosis0.65263933
Mean8.0085352
Median Absolute Deviation (MAD)0.24
Skewness0.43386813
Sum2843.03
Variance0.14553627
MonotonicityNot monotonic
2023-08-16T18:07:38.410386image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.99 10
 
2.8%
8.09 8
 
2.3%
8.28 8
 
2.3%
7.94 8
 
2.3%
8.04 7
 
2.0%
7.81 7
 
2.0%
7.76 6
 
1.7%
8.32 6
 
1.7%
7.84 6
 
1.7%
8.23 6
 
1.7%
Other values (127) 283
79.7%
ValueCountFrequency (%)
7.02 1
0.3%
7.17 1
0.3%
7.22 1
0.3%
7.26 2
0.6%
7.27 1
0.3%
7.29 2
0.6%
7.32 2
0.6%
7.33 1
0.3%
7.35 1
0.3%
7.38 1
0.3%
ValueCountFrequency (%)
9.37 1
0.3%
9.32 1
0.3%
9.17 1
0.3%
9.1 1
0.3%
9.08 1
0.3%
9.06 1
0.3%
8.87 1
0.3%
8.85 1
0.3%
8.84 1
0.3%
8.83 1
0.3%

E.C
Real number (ℝ)

Distinct322
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1268.9651
Minimum102
Maximum5175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-16T18:07:38.801043image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile446.9
Q1768
median1085
Q31590
95-th percentile2550
Maximum5175
Range5073
Interquartile range (IQR)822

Descriptive statistics

Standard deviation746.19162
Coefficient of variation (CV)0.58803165
Kurtosis5.5917829
Mean1268.9651
Median Absolute Deviation (MAD)368
Skewness1.9326062
Sum450482.6
Variance556801.93
MonotonicityNot monotonic
2023-08-16T18:07:39.238541image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
788 3
 
0.8%
1590 2
 
0.6%
976 2
 
0.6%
382 2
 
0.6%
2636 2
 
0.6%
1960 2
 
0.6%
768 2
 
0.6%
464 2
 
0.6%
1369 2
 
0.6%
1302 2
 
0.6%
Other values (312) 334
94.1%
ValueCountFrequency (%)
102 1
0.3%
111 1
0.3%
250 1
0.3%
306 1
0.3%
316 1
0.3%
350 1
0.3%
367 1
0.3%
382 2
0.6%
391 1
0.3%
404 1
0.3%
ValueCountFrequency (%)
5175 1
0.3%
4878 1
0.3%
4429 1
0.3%
4377 1
0.3%
4264 1
0.3%
3961 1
0.3%
3717 1
0.3%
3553 1
0.3%
3189 1
0.3%
3171 1
0.3%

TDS
Real number (ℝ)

Distinct322
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean812.13765
Minimum65.28
Maximum3312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-16T18:07:39.644761image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum65.28
5-th percentile286.016
Q1491.52
median694.4
Q31017.6
95-th percentile1632
Maximum3312
Range3246.72
Interquartile range (IQR)526.08

Descriptive statistics

Standard deviation477.56264
Coefficient of variation (CV)0.58803165
Kurtosis5.5917829
Mean812.13765
Median Absolute Deviation (MAD)235.52
Skewness1.9326062
Sum288308.86
Variance228066.07
MonotonicityNot monotonic
2023-08-16T18:07:40.035384image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
504.32 3
 
0.8%
1017.6 2
 
0.6%
624.64 2
 
0.6%
244.48 2
 
0.6%
1687.04 2
 
0.6%
1254.4 2
 
0.6%
491.52 2
 
0.6%
296.96 2
 
0.6%
876.16 2
 
0.6%
833.28 2
 
0.6%
Other values (312) 334
94.1%
ValueCountFrequency (%)
65.28 1
0.3%
71.04 1
0.3%
160 1
0.3%
195.84 1
0.3%
202.24 1
0.3%
224 1
0.3%
234.88 1
0.3%
244.48 2
0.6%
250.24 1
0.3%
258.56 1
0.3%
ValueCountFrequency (%)
3312 1
0.3%
3121.92 1
0.3%
2834.56 1
0.3%
2801.28 1
0.3%
2728.96 1
0.3%
2535.04 1
0.3%
2378.88 1
0.3%
2273.92 1
0.3%
2040.96 1
0.3%
2029.44 1
0.3%

CO3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1267606
Minimum0
Maximum80
Zeros292
Zeros (%)82.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-16T18:07:40.426025image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile30
Maximum80
Range80
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.829752
Coefficient of variation (CV)2.5025065
Kurtosis9.2108983
Mean5.1267606
Median Absolute Deviation (MAD)0
Skewness2.9056443
Sum1820
Variance164.60253
MonotonicityNot monotonic
2023-08-16T18:07:40.754154image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 292
82.3%
20 20
 
5.6%
30 16
 
4.5%
10 10
 
2.8%
40 8
 
2.3%
50 6
 
1.7%
80 2
 
0.6%
60 1
 
0.3%
ValueCountFrequency (%)
0 292
82.3%
10 10
 
2.8%
20 20
 
5.6%
30 16
 
4.5%
40 8
 
2.3%
50 6
 
1.7%
60 1
 
0.3%
80 2
 
0.6%
ValueCountFrequency (%)
80 2
 
0.6%
60 1
 
0.3%
50 6
 
1.7%
40 8
 
2.3%
30 16
 
4.5%
20 20
 
5.6%
10 10
 
2.8%
0 292
82.3%

HCO3
Real number (ℝ)

Distinct56
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean279.43662
Minimum20
Maximum950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-16T18:07:41.144755image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile107
Q1190
median270
Q3350
95-th percentile490
Maximum950
Range930
Interquartile range (IQR)160

Descriptive statistics

Standard deviation123.44499
Coefficient of variation (CV)0.44176381
Kurtosis2.6096678
Mean279.43662
Median Absolute Deviation (MAD)80
Skewness0.96739325
Sum99200
Variance15238.665
MonotonicityNot monotonic
2023-08-16T18:07:41.535388image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 17
 
4.8%
180 17
 
4.8%
290 17
 
4.8%
230 15
 
4.2%
260 14
 
3.9%
220 13
 
3.7%
330 13
 
3.7%
130 12
 
3.4%
350 12
 
3.4%
360 11
 
3.1%
Other values (46) 214
60.3%
ValueCountFrequency (%)
20 1
 
0.3%
30 1
 
0.3%
70 1
 
0.3%
80 2
 
0.6%
90 5
1.4%
100 8
2.3%
110 4
 
1.1%
120 11
3.1%
130 12
3.4%
140 4
 
1.1%
ValueCountFrequency (%)
950 1
 
0.3%
810 1
 
0.3%
620 1
 
0.3%
590 2
0.6%
580 1
 
0.3%
570 2
0.6%
550 3
0.8%
530 2
0.6%
520 1
 
0.3%
510 1
 
0.3%

Cl
Real number (ℝ)

Distinct65
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193.07042
Minimum10
Maximum1380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-16T18:07:41.941640image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile30
Q170
median140
Q3250
95-th percentile530
Maximum1380
Range1370
Interquartile range (IQR)180

Descriptive statistics

Standard deviation185.59382
Coefficient of variation (CV)0.96127525
Kurtosis8.8530493
Mean193.07042
Median Absolute Deviation (MAD)80
Skewness2.5178493
Sum68540
Variance34445.066
MonotonicityNot monotonic
2023-08-16T18:07:42.332258image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 21
 
5.9%
70 20
 
5.6%
50 16
 
4.5%
30 15
 
4.2%
40 15
 
4.2%
80 14
 
3.9%
110 13
 
3.7%
90 13
 
3.7%
120 13
 
3.7%
220 12
 
3.4%
Other values (55) 203
57.2%
ValueCountFrequency (%)
10 2
 
0.6%
20 11
3.1%
30 15
4.2%
40 15
4.2%
50 16
4.5%
60 21
5.9%
70 20
5.6%
80 14
3.9%
90 13
3.7%
100 10
2.8%
ValueCountFrequency (%)
1380 1
0.3%
1010 2
0.6%
980 1
0.3%
960 1
0.3%
950 1
0.3%
890 1
0.3%
820 1
0.3%
760 1
0.3%
740 1
0.3%
670 1
0.3%

F
Real number (ℝ)

Distinct189
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1069155
Minimum0.04
Maximum4.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-16T18:07:42.754140image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.28
Q10.6
median0.89
Q31.44
95-th percentile2.427
Maximum4.78
Range4.74
Interquartile range (IQR)0.84

Descriptive statistics

Standard deviation0.74635962
Coefficient of variation (CV)0.67426973
Kurtosis3.5681416
Mean1.1069155
Median Absolute Deviation (MAD)0.35
Skewness1.6080648
Sum392.955
Variance0.55705268
MonotonicityNot monotonic
2023-08-16T18:07:43.097922image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.62 8
 
2.3%
0.45 6
 
1.7%
0.59 5
 
1.4%
0.61 5
 
1.4%
1.05 5
 
1.4%
0.72 5
 
1.4%
0.83 5
 
1.4%
0.89 5
 
1.4%
0.54 4
 
1.1%
0.82 4
 
1.1%
Other values (179) 303
85.4%
ValueCountFrequency (%)
0.04 1
 
0.3%
0.115 1
 
0.3%
0.14 1
 
0.3%
0.147 1
 
0.3%
0.18 2
0.6%
0.19 1
 
0.3%
0.2 3
0.8%
0.21 1
 
0.3%
0.23 2
0.6%
0.257 1
 
0.3%
ValueCountFrequency (%)
4.78 1
0.3%
4.57 1
0.3%
4.23 1
0.3%
3.64 1
0.3%
3.39 1
0.3%
3.26 1
0.3%
3.19 1
0.3%
3.17 1
0.3%
3.15 1
0.3%
3.11 1
0.3%

NO3
Real number (ℝ)

Distinct321
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.077702
Minimum0.026574
Maximum456.187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-16T18:07:43.536130image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.026574
5-th percentile1.7864975
Q115.262334
median35.432
Q371.57264
95-th percentile211.66191
Maximum456.187
Range456.16043
Interquartile range (IQR)56.310306

Descriptive statistics

Standard deviation74.767623
Coefficient of variation (CV)1.2445154
Kurtosis9.1953007
Mean60.077702
Median Absolute Deviation (MAD)21.7021
Skewness2.7004786
Sum21327.584
Variance5590.1975
MonotonicityNot monotonic
2023-08-16T18:07:43.974398image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.432 4
 
1.1%
0.805272727 3
 
0.8%
13.7299 3
 
0.8%
19.66476 3
 
0.8%
28.83279 2
 
0.6%
213.21206 2
 
0.6%
5.032954545 2
 
0.6%
31.4459 2
 
0.6%
62.40863636 2
 
0.6%
90.7945 2
 
0.6%
Other values (311) 330
93.0%
ValueCountFrequency (%)
0.026574 1
 
0.3%
0.048719 1
 
0.3%
0.17716 1
 
0.3%
0.26574 1
 
0.3%
0.314459 1
 
0.3%
0.35432 1
 
0.3%
0.402636364 1
 
0.3%
0.805272727 3
0.8%
1.06296 1
 
0.3%
1.10725 1
 
0.3%
ValueCountFrequency (%)
456.187 1
0.3%
451.758 1
0.3%
446.53178 1
0.3%
436.6994 1
0.3%
414.1115 1
0.3%
326.90449 1
0.3%
301.172 1
0.3%
298.9575 1
0.3%
297.6288 1
0.3%
244.79083 1
0.3%

SO4
Real number (ℝ)

Distinct145
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.671915
Minimum3.75
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-16T18:07:44.428976image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3.75
5-th percentile8
Q113
median20
Q329.5
95-th percentile56.6
Maximum440
Range436.25
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation29.58777
Coefficient of variation (CV)1.1525346
Kurtosis111.62046
Mean25.671915
Median Absolute Deviation (MAD)7.75
Skewness8.7959976
Sum9113.53
Variance875.43615
MonotonicityNot monotonic
2023-08-16T18:07:44.929055image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 14
 
3.9%
20 14
 
3.9%
13 13
 
3.7%
23 11
 
3.1%
24 11
 
3.1%
12 10
 
2.8%
10 10
 
2.8%
14 10
 
2.8%
22 9
 
2.5%
15 8
 
2.3%
Other values (135) 245
69.0%
ValueCountFrequency (%)
3.75 1
 
0.3%
4.25 1
 
0.3%
4.5 1
 
0.3%
5 2
 
0.6%
5.95 1
 
0.3%
6 1
 
0.3%
7 5
1.4%
7.25 1
 
0.3%
7.45 1
 
0.3%
7.75 1
 
0.3%
ValueCountFrequency (%)
440 1
0.3%
180 1
0.3%
158 1
0.3%
107.29 1
0.3%
105 1
0.3%
98.5 1
0.3%
98 1
0.3%
96 1
0.3%
94 1
0.3%
86 1
0.3%

Na
Real number (ℝ)

Distinct258
Distinct (%)72.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.80851
Minimum6.4
Maximum712
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-16T18:07:45.366495image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum6.4
5-th percentile26.472
Q155.16
median88.18
Q3147.5
95-th percentile312.7
Maximum712
Range705.6
Interquartile range (IQR)92.34

Descriptive statistics

Standard deviation100.11666
Coefficient of variation (CV)0.85710077
Kurtosis7.8171247
Mean116.80851
Median Absolute Deviation (MAD)37.44
Skewness2.4076382
Sum41467.02
Variance10023.346
MonotonicityNot monotonic
2023-08-16T18:07:45.804986image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 6
 
1.7%
62 6
 
1.7%
72 5
 
1.4%
55 5
 
1.4%
115 5
 
1.4%
29 5
 
1.4%
60 5
 
1.4%
112 4
 
1.1%
69 4
 
1.1%
61 4
 
1.1%
Other values (248) 306
86.2%
ValueCountFrequency (%)
6.4 1
0.3%
10 1
0.3%
12 2
0.6%
16 2
0.6%
17 1
0.3%
17.58 1
0.3%
19 1
0.3%
20 2
0.6%
20.9 1
0.3%
21 1
0.3%
ValueCountFrequency (%)
712 1
0.3%
623 1
0.3%
535 1
0.3%
532 1
0.3%
515 1
0.3%
511 1
0.3%
486 1
0.3%
458.2 1
0.3%
402.6 1
0.3%
381.2 1
0.3%

K
Real number (ℝ)

Distinct192
Distinct (%)54.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1417465
Minimum0.18
Maximum95.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-16T18:07:46.336261image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.18
5-th percentile0.931
Q12
median3
Q35
95-th percentile20.615
Maximum95.8
Range95.62
Interquartile range (IQR)3

Descriptive statistics

Standard deviation11.171399
Coefficient of variation (CV)1.8189287
Kurtosis32.670703
Mean6.1417465
Median Absolute Deviation (MAD)1.27
Skewness5.2387933
Sum2180.32
Variance124.80015
MonotonicityNot monotonic
2023-08-16T18:07:46.805061image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 30
 
8.5%
3 23
 
6.5%
1 19
 
5.4%
4 19
 
5.4%
5 12
 
3.4%
6 6
 
1.7%
12 5
 
1.4%
3.9 4
 
1.1%
7 4
 
1.1%
3.2 4
 
1.1%
Other values (182) 229
64.5%
ValueCountFrequency (%)
0.18 1
 
0.3%
0.2 1
 
0.3%
0.24 1
 
0.3%
0.34 1
 
0.3%
0.49 2
0.6%
0.56 2
0.6%
0.6 3
0.8%
0.62 1
 
0.3%
0.63 1
 
0.3%
0.68 2
0.6%
ValueCountFrequency (%)
95.8 1
0.3%
89.18 1
0.3%
89 1
0.3%
66.61 1
0.3%
63.3 1
0.3%
46.11 1
0.3%
44.4 1
0.3%
43 1
0.3%
37.5 1
0.3%
36.9 1
0.3%

Ca
Real number (ℝ)

Distinct30
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.216901
Minimum8
Maximum488
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-16T18:07:47.211377image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile16
Q140
median64
Q388
95-th percentile176
Maximum488
Range480
Interquartile range (IQR)48

Descriptive statistics

Standard deviation53.449229
Coefficient of variation (CV)0.73001217
Kurtosis13.725677
Mean73.216901
Median Absolute Deviation (MAD)24
Skewness2.6764095
Sum25992
Variance2856.8201
MonotonicityNot monotonic
2023-08-16T18:07:47.570687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
40 44
12.4%
56 32
 
9.0%
64 30
 
8.5%
32 26
 
7.3%
80 26
 
7.3%
24 25
 
7.0%
72 24
 
6.8%
48 20
 
5.6%
16 18
 
5.1%
88 18
 
5.1%
Other values (20) 92
25.9%
ValueCountFrequency (%)
8 6
 
1.7%
16 18
5.1%
24 25
7.0%
32 26
7.3%
40 44
12.4%
48 20
5.6%
56 32
9.0%
64 30
8.5%
72 24
6.8%
80 26
7.3%
ValueCountFrequency (%)
488 1
 
0.3%
400 1
 
0.3%
264 1
 
0.3%
216 1
 
0.3%
208 3
0.8%
200 3
0.8%
192 3
0.8%
184 4
1.1%
176 5
1.4%
168 2
 
0.6%

Mg
Real number (ℝ)

Distinct36
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.773555
Minimum4.862
Maximum233.376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-16T18:07:47.961318image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum4.862
5-th percentile13.1274
Q124.31
median43.758
Q363.206
95-th percentile116.688
Maximum233.376
Range228.514
Interquartile range (IQR)38.896

Descriptive statistics

Standard deviation34.770786
Coefficient of variation (CV)0.69857951
Kurtosis5.3424665
Mean49.773555
Median Absolute Deviation (MAD)19.448
Skewness1.8615011
Sum17669.612
Variance1209.0075
MonotonicityNot monotonic
2023-08-16T18:07:48.336328image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
38.896 29
 
8.2%
19.448 29
 
8.2%
24.31 29
 
8.2%
34.034 28
 
7.9%
48.62 24
 
6.8%
29.172 22
 
6.2%
14.586 21
 
5.9%
43.758 19
 
5.4%
53.482 18
 
5.1%
63.206 17
 
4.8%
Other values (26) 119
33.5%
ValueCountFrequency (%)
4.862 8
 
2.3%
9.724 10
 
2.8%
14.586 21
5.9%
19.448 29
8.2%
24.31 29
8.2%
29.172 22
6.2%
34.034 28
7.9%
38.896 29
8.2%
40 1
 
0.3%
43.758 19
5.4%
ValueCountFrequency (%)
233.376 2
0.6%
194.48 1
 
0.3%
175.032 1
 
0.3%
160.446 1
 
0.3%
155.584 3
0.8%
150.722 1
 
0.3%
145.86 1
 
0.3%
140.998 1
 
0.3%
131.274 1
 
0.3%
126.412 1
 
0.3%

T.H
Real number (ℝ)

Distinct223
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean387.49201
Minimum39.991776
Maximum1939.7039
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-16T18:07:48.758199image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum39.991776
5-th percentile139.97533
Q1239.94243
median339.90132
Q3479.91365
95-th percentile785.86102
Maximum1939.7039
Range1899.7122
Interquartile range (IQR)239.97122

Descriptive statistics

Standard deviation227.51835
Coefficient of variation (CV)0.58715624
Kurtosis8.5017002
Mean387.49201
Median Absolute Deviation (MAD)119.95066
Skewness2.1639557
Sum137559.66
Variance51764.6
MonotonicityNot monotonic
2023-08-16T18:07:49.180061image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
259.9342105 7
 
2.0%
179.9671053 6
 
1.7%
339.9177632 6
 
1.7%
219.9506579 5
 
1.4%
359.9177632 5
 
1.4%
319.9506579 4
 
1.1%
199.9588816 4
 
1.1%
279.9424342 4
 
1.1%
99.97532895 4
 
1.1%
139.9753289 4
 
1.1%
Other values (213) 306
86.2%
ValueCountFrequency (%)
39.99177632 1
 
0.3%
59.99177632 1
 
0.3%
79.97532895 1
 
0.3%
99.97532895 4
1.1%
99.98355263 3
0.8%
119.9671053 3
0.8%
119.9835526 1
 
0.3%
139.9588816 1
 
0.3%
139.9671053 2
0.6%
139.9753289 4
1.1%
ValueCountFrequency (%)
1939.703947 1
0.3%
1459.671053 1
0.3%
1399.605263 1
0.3%
1319.868421 1
0.3%
1119.736842 1
0.3%
1019.728618 1
0.3%
1019.605263 1
0.3%
979.7779605 1
0.3%
959.7368421 1
0.3%
899.8355263 1
0.3%

SAR
Real number (ℝ)

Distinct351
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6953978
Minimum0.27828376
Maximum16.707222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-08-16T18:07:49.633190image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.27828376
5-th percentile0.65424917
Q11.3156781
median2.0515813
Q33.1160711
95-th percentile7.2470534
Maximum16.707222
Range16.428939
Interquartile range (IQR)1.800393

Descriptive statistics

Standard deviation2.3141746
Coefficient of variation (CV)0.85856512
Kurtosis10.056328
Mean2.6953978
Median Absolute Deviation (MAD)0.82264498
Skewness2.7506622
Sum956.86622
Variance5.3554039
MonotonicityNot monotonic
2023-08-16T18:07:50.070688image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.021273363 2
 
0.6%
1.131686033 2
 
0.6%
1.475852828 2
 
0.6%
2.257357044 2
 
0.6%
3.288777535 1
 
0.3%
2.00022619 1
 
0.3%
8.525785469 1
 
0.3%
1.467973983 1
 
0.3%
1.870957465 1
 
0.3%
1.380744683 1
 
0.3%
Other values (341) 341
96.1%
ValueCountFrequency (%)
0.278283756 1
0.3%
0.388916371 1
0.3%
0.388916926 1
0.3%
0.39723036 1
0.3%
0.412513406 1
0.3%
0.481271139 1
0.3%
0.550003737 1
0.3%
0.552308647 1
0.3%
0.55701018 1
0.3%
0.561340405 1
0.3%
ValueCountFrequency (%)
16.7072224 1
0.3%
15.71039105 1
0.3%
14.00172729 1
0.3%
13.88781532 1
0.3%
11.29978234 1
0.3%
10.56903406 1
0.3%
10.23941103 1
0.3%
9.992544115 1
0.3%
9.808271921 1
0.3%
9.532333618 1
0.3%

Classification
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
C3S1
227 
C2S1
77 
C4S1
 
17
C3S2
 
14
C4S2
 
11
Other values (5)
 
9

Length

Max length4
Median length4
Mean length3.9971831
Min length3

Characters and Unicode

Total characters1419
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowC3S1
2nd rowC2S1
3rd rowC2S1
4th rowC3S1
5th rowC3S1

Common Values

ValueCountFrequency (%)
C3S1 227
63.9%
C2S1 77
 
21.7%
C4S1 17
 
4.8%
C3S2 14
 
3.9%
C4S2 11
 
3.1%
C4S4 2
 
0.6%
C4S3 2
 
0.6%
C1S1 2
 
0.6%
C3S3 2
 
0.6%
O.G 1
 
0.3%

Length

2023-08-16T18:07:50.493197image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-16T18:07:50.931356image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
c3s1 227
63.9%
c2s1 77
 
21.7%
c4s1 17
 
4.8%
c3s2 14
 
3.9%
c4s2 11
 
3.1%
c4s4 2
 
0.6%
c4s3 2
 
0.6%
c1s1 2
 
0.6%
c3s3 2
 
0.6%
o.g 1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
C 354
24.9%
S 354
24.9%
1 325
22.9%
3 247
17.4%
2 102
 
7.2%
4 34
 
2.4%
O 1
 
0.1%
. 1
 
0.1%
G 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 710
50.0%
Decimal Number 708
49.9%
Other Punctuation 1
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 354
49.9%
S 354
49.9%
O 1
 
0.1%
G 1
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 325
45.9%
3 247
34.9%
2 102
 
14.4%
4 34
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 710
50.0%
Common 709
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 325
45.8%
3 247
34.8%
2 102
 
14.4%
4 34
 
4.8%
. 1
 
0.1%
Latin
ValueCountFrequency (%)
C 354
49.9%
S 354
49.9%
O 1
 
0.1%
G 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1419
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 354
24.9%
S 354
24.9%
1 325
22.9%
3 247
17.4%
2 102
 
7.2%
4 34
 
2.4%
O 1
 
0.1%
. 1
 
0.1%
G 1
 
0.1%

RSC meq / L
Real number (ℝ)

Distinct295
Distinct (%)83.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.0616711
Minimum-31.194079
Maximum16.600822
Zeros0
Zeros (%)0.0%
Negative240
Negative (%)67.6%
Memory size2.9 KiB
2023-08-16T18:07:51.353355image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-31.194079
5-th percentile-10.257993
Q1-3.5973684
median-1.3981908
Q30.40041118
95-th percentile2.8018914
Maximum16.600822
Range47.794901
Interquartile range (IQR)3.9977796

Descriptive statistics

Standard deviation4.5428899
Coefficient of variation (CV)-2.2034988
Kurtosis7.1166176
Mean-2.0616711
Median Absolute Deviation (MAD)1.8014803
Skewness-1.3809146
Sum-731.89326
Variance20.637849
MonotonicityNot monotonic
2023-08-16T18:07:51.775156image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.600986842 3
 
0.8%
-1.598355263 3
 
0.8%
-0.198026316 3
 
0.8%
0.800822368 3
 
0.8%
-0.998684211 3
 
0.8%
-0.199013158 3
 
0.8%
-1.798684211 3
 
0.8%
-1.398848684 3
 
0.8%
0.201480263 3
 
0.8%
-0.399671053 2
 
0.6%
Other values (285) 326
91.8%
ValueCountFrequency (%)
-31.19407895 1
0.3%
-20.79342105 1
0.3%
-19.39736842 1
0.3%
-18.19210526 1
0.3%
-16.99473684 1
0.3%
-15.59605263 1
0.3%
-15.59457237 1
0.3%
-13.79555921 1
0.3%
-12.396875 1
0.3%
-12.39572368 1
0.3%
ValueCountFrequency (%)
16.60082237 1
0.3%
11.00098684 1
0.3%
9.800493421 1
0.3%
8.800493421 1
0.3%
8.200657895 1
0.3%
7.400493421 1
0.3%
7.001809211 1
0.3%
5.801480263 1
0.3%
5.601480263 1
0.3%
5.600164474 1
0.3%

Classification.1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
P.S.
313 
U.S.
 
23
MR
 
16
M.R
 
3

Length

Max length4
Median length4
Mean length3.9014085
Min length2

Characters and Unicode

Total characters1385
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP.S.
2nd rowMR
3rd rowP.S.
4th rowP.S.
5th rowP.S.

Common Values

ValueCountFrequency (%)
P.S. 313
88.2%
U.S. 23
 
6.5%
MR 16
 
4.5%
M.R 3
 
0.8%

Length

2023-08-16T18:07:52.165785image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-16T18:07:52.603281image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
p.s 313
88.2%
u.s 23
 
6.5%
mr 16
 
4.5%
m.r 3
 
0.8%

Most occurring characters

ValueCountFrequency (%)
. 675
48.7%
S 336
24.3%
P 313
22.6%
U 23
 
1.7%
M 19
 
1.4%
R 19
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 710
51.3%
Other Punctuation 675
48.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 336
47.3%
P 313
44.1%
U 23
 
3.2%
M 19
 
2.7%
R 19
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 675
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 710
51.3%
Common 675
48.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 336
47.3%
P 313
44.1%
U 23
 
3.2%
M 19
 
2.7%
R 19
 
2.7%
Common
ValueCountFrequency (%)
. 675
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1385
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 675
48.7%
S 336
24.3%
P 313
22.6%
U 23
 
1.7%
M 19
 
1.4%
R 19
 
1.4%

Interactions

2023-08-16T18:07:23.534750image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:05:05.781197image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-08-16T18:05:26.701690image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-08-16T18:06:00.922238image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-08-16T18:06:14.581810image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-08-16T18:07:22.565990image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:07:29.160429image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:05:12.043200image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:05:19.045451image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:05:26.045442image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:05:33.233659image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:05:40.171098image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:05:46.421227image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:05:52.718107image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:05:59.609754image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:06:06.579412image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:06:13.174078image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:06:20.832410image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:06:28.298548image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:06:35.199459image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:06:41.351572image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:06:48.646204image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:06:55.335566image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:07:02.738861image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:07:09.675391image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:07:15.612854image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:07:22.894118image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:07:29.488520image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:05:12.418133image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:05:19.389226image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:05:26.389191image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:05:33.592968image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:05:40.483611image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:05:46.733732image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:05:53.030618image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:05:59.953503image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:06:06.954439image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:06:13.486644image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:06:21.207461image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:06:28.631103image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:06:35.496333image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:06:41.648449image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:06:48.978330image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:06:55.743883image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:07:03.161416image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:07:09.925409image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:07:16.034730image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-16T18:07:23.222241image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-08-16T18:07:52.993902image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
snotemp_idlong_gislat_gisgwlpHE.CTDSCO3HCO3ClFNO3SO4NaKCaMgT.HSARRSC meq / LdistrictClassificationClassification.1
sno1.0000.849-0.117-0.1670.068-0.0610.0310.031-0.065-0.0790.0730.1200.0380.017-0.049-0.2000.1080.0550.095-0.101-0.1510.8880.0810.135
temp_id0.8491.0000.039-0.1340.0550.016-0.014-0.0140.036-0.1120.0220.0840.012-0.023-0.082-0.1430.0390.0120.037-0.121-0.1240.6160.0140.113
long_gis-0.1170.0391.0000.173-0.3410.175-0.108-0.1080.099-0.106-0.091-0.056-0.0290.035-0.0040.252-0.293-0.034-0.1920.0730.1310.6340.0720.103
lat_gis-0.167-0.1340.1731.000-0.019-0.114-0.307-0.307-0.183-0.119-0.277-0.108-0.098-0.212-0.2610.112-0.251-0.035-0.165-0.1820.0950.7520.1310.178
gwl0.0680.055-0.341-0.0191.000-0.1570.0130.013-0.0790.0260.0030.0310.090-0.204-0.087-0.0350.200-0.0280.107-0.145-0.0960.2380.0000.000
pH-0.0610.0160.175-0.114-0.1571.000-0.189-0.1890.661-0.194-0.2120.146-0.210-0.0890.034-0.015-0.533-0.177-0.4410.1830.3640.2340.2090.270
E.C0.031-0.014-0.108-0.3070.013-0.1891.0001.000-0.0760.6080.8890.1910.4540.6470.8040.3120.5380.6760.7740.553-0.4040.0350.4870.039
TDS0.031-0.014-0.108-0.3070.013-0.1891.0001.000-0.0760.6080.8890.1910.4540.6470.8040.3120.5380.6760.7740.553-0.4040.0350.4870.039
CO3-0.0650.0360.099-0.183-0.0790.661-0.076-0.0761.000-0.069-0.1210.119-0.156-0.0260.1330.051-0.407-0.150-0.3260.2350.2990.1950.1380.238
HCO3-0.079-0.112-0.106-0.1190.026-0.1940.6080.608-0.0691.0000.2910.4120.0980.2000.5660.1400.2490.3900.4190.4380.2110.1570.4680.308
Cl0.0730.022-0.091-0.2770.003-0.2120.8890.889-0.1210.2911.0000.0530.4280.6680.6960.3300.5430.6420.7540.463-0.6020.0000.4030.000
F0.1200.084-0.056-0.1080.0310.1460.1910.1910.1190.4120.0531.000-0.065-0.0270.3120.003-0.0750.0690.0010.3170.2750.1340.2390.293
NO30.0380.012-0.029-0.0980.090-0.2100.4540.454-0.1560.0980.428-0.0651.0000.3050.2230.2610.4050.3670.5050.045-0.5100.0850.2600.000
SO40.017-0.0230.035-0.212-0.204-0.0890.6470.647-0.0260.2000.668-0.0270.3051.0000.5760.2160.3110.5000.4990.429-0.3930.1280.5170.027
Na-0.049-0.082-0.004-0.261-0.0870.0340.8040.8040.1330.5660.6960.3120.2230.5761.0000.3210.1490.4050.3490.9280.0180.0060.6230.306
K-0.200-0.1430.2520.112-0.035-0.0150.3120.3120.0510.1400.3300.0030.2610.2160.3211.0000.0800.1660.1730.290-0.0860.0750.1720.035
Ca0.1080.039-0.293-0.2510.200-0.5330.5380.538-0.4070.2490.543-0.0750.4050.3110.1490.0801.0000.2780.782-0.118-0.6310.1750.2380.040
Mg0.0550.012-0.034-0.035-0.028-0.1770.6760.676-0.1500.3900.6420.0690.3670.5000.4050.1660.2781.0000.7740.154-0.5320.0000.2990.000
T.H0.0950.037-0.192-0.1650.107-0.4410.7740.774-0.3260.4190.7540.0010.5050.4990.3490.1730.7820.7741.0000.023-0.7420.0720.3690.063
SAR-0.101-0.1210.073-0.182-0.1450.1830.5530.5530.2350.4380.4630.3170.0450.4290.9280.290-0.1180.1540.0231.0000.2810.1010.5250.433
RSC meq / L-0.151-0.1240.1310.095-0.0960.364-0.404-0.4040.2990.211-0.6020.275-0.510-0.3930.018-0.086-0.631-0.532-0.7420.2811.0000.0000.4350.548
district0.8880.6160.6340.7520.2380.2340.0350.0350.1950.1570.0000.1340.0850.1280.0060.0750.1750.0000.0720.1010.0001.0000.1450.343
Classification0.0810.0140.0720.1310.0000.2090.4870.4870.1380.4680.4030.2390.2600.5170.6230.1720.2380.2990.3690.5250.4350.1451.0000.382
Classification.10.1350.1130.1030.1780.0000.2700.0390.0390.2380.3080.0000.2930.0000.0270.3060.0350.0400.0000.0630.4330.5480.3430.3821.000

Missing values

2023-08-16T18:07:30.051017image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-16T18:07:31.176019image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

snodistrictmandalvillagetemp_idlong_gislat_gisgwlseasonpHE.CTDSCO3HCO3ClFNO3SO4NaKCaMgT.HSARClassificationRSC meq / LClassification.1
01ADILABADAdilabadAdilabad100178.52470019.66830014.15Pre-monsoon 20207.801671.01069.4404702300.5103.42240931.50154.013.007277.792499.8684212.994781C3S1-0.597368P.S.
12ADILABADBazarhatnurBazarhatnur100278.35083319.4588889.35Pre-monsoon 20207.95545.0348.800180801.0804.22768213.0094.012.001614.58699.9753294.087461C2S11.600493MR
23ADILABADGudihatnoorGudihatnoor100778.51222219.52555517.50Pre-monsoon 20208.12738.0472.320220300.45028.3858649.5022.01.004038.896259.9342110.593285C2S1-0.798684P.S.
34ADILABADJainathJainath100978.64000019.7305555.30Pre-monsoon 20208.051154.0738.5603001000.690140.92272714.7581.03.005668.068419.8848681.718668C3S1-2.397697P.S.
45ADILABADNarnoorNarnoor101078.85265419.4956654.50Pre-monsoon 20207.171042.0666.880370300.720163.06772710.2539.04.009663.206499.8930920.758400C3S1-2.597862P.S.
56ADILABADNeradigondaNeradigonda101178.41216019.2937509.90Pre-monsoon 20208.091063.0680.3203001000.56062.40863614.5065.014.006453.482379.9095391.449925C3S1-1.598191P.S.
67ADILABADTalamaduguTalamadugu101378.39690019.6334008.10Pre-monsoon 20207.94788.0504.320310400.44042.07550010.5059.04.004043.758279.9259871.533213C3S10.601480P.S.
78ADILABADTamsiTamsi101478.42680019.6811006.30Pre-monsoon 20208.32811.0519.04202001100.73011.47513616.00144.01.001614.58699.9753296.261642C3S12.400493MR
89ADILABADUtnoorUtnoor101578.76850019.3789006.55Pre-monsoon 20207.99422.0270.080160300.45013.4883188.2541.02.002419.448139.9671051.506756C2S10.400658P.S.
910BHADRADRIAnnapureddypalliAnnapureddypalli102180.82610417.35451917.30Pre-monsoon 20208.28250.0160.00090100.1475.3148006.006.41.77249.72499.9835530.278284C2S1-0.199671P.S.
snodistrictmandalvillagetemp_idlong_gislat_gisgwlseasonpHE.CTDSCO3HCO3ClFNO3SO4NaKCaMgT.HSARClassificationRSC meq / LClassification.1
345370YADADRIRajapetBondugala187278.96650017.7683059.80Pre-monsoon 20208.281052.0673.2802601702.226.1120220.099.202.995643.758319.9259872.411343C3S1-1.198520P.S.
346371YADADRIRajapetKurraram187378.92986617.77549017.34Pre-monsoon 20207.271009.0645.7602401300.7565.1063023.055.003.3511224.310379.9588821.226780C3S1-2.799178P.S.
347372YADADRIRajapetRajapet187578.93296817.7294317.38Pre-monsoon 20207.221408.0901.1202303001.7036.9378619.076.473.7413648.620539.9177631.430868C3S1-6.198355P.S.
348373YADADRIRajapetSomaram187678.99178517.72567116.58Pre-monsoon 20207.66708.0453.120290402.3014.7042810.043.552.775634.034279.9424341.131686C2S10.201151P.S.
349374YADADRIRamannapetRamanapet187779.09509117.28312112.60Pre-monsoon 20207.673171.02029.4402708901.7235.4320030.0215.602.50192155.5841119.7368422.801321C4S1-16.994737P.S.
350375YADADRIS.NarayanpurS.Narayanpur188078.86001017.14471925.90Pre-monsoon 20207.402204.01410.5602903001.11436.6994032.0148.002.00144102.102779.8273032.304277C3S1-9.796546P.S.
351376YADADRIThurkapallyGandamalla188178.85383117.7331016.73Pre-monsoon 20207.881363.0872.3203801702.2335.6091625.0119.005.397258.344419.9013162.524908C3S1-0.798026P.S.
352377YADADRIValigondaT. somaram188378.95229017.39995313.62Pre-monsoon 20207.66708.0453.120290402.3014.7042810.043.552.775634.034279.9424341.131686C2S10.201151P.S.
353378YADADRIValigondaVemulakonda188579.14343317.3477824.07Pre-monsoon 20207.634878.03121.92035013800.8036.89357105.0532.007.3040077.7921319.8684216.366760C4S2-19.397368P.S.
354379YADADRIY.GuttaMallapuram188678.91163817.63355516.78Pre-monsoon 20207.461614.01032.9602503501.7744.9986422.094.003.7415253.482599.9095401.668619C3S1-6.998191P.S.